Showing papers by "Nagarajan Kandasamy published in 2008"

Power and Performance Management of Virtualized Computing Environments Via Lookahead Control

Abstract: There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 26% of the power required by a system without dynamic control while still maintaining QoS goals.

Approximation Modeling for the Online Performance Management of Distributed Computing Systems

Abstract: A promising method of automating management tasks in computing systems is to formulate them as control or optimization problems in terms of performance metrics For an online optimization scheme to be of practical value in a distributed setting, however, it must successfully tackle the curses of dimensionality and modeling This paper develops a hierarchical control framework to solve performance management problems in distributed computing systems operating in a data center Concepts from approximation theory are used to reduce the computational burden of controlling such large-scale systems The relevant approximations are made in the construction of the dynamical models to predict system behavior and in the solution of the associated control equations Using a dynamic resource-provisioning problem as a case study, we show that a computing system managed by the proposed control framework with approximation models realizes profit gains that are, in the best case, within 1% of a controller using an explicit model of the system